The comparison of accuracy between naïve bayes clasifier and c4.5 algorithm in classifying toddler nutrition status based on anthropometry index. Issue 1 (February 2021)

• Record Type: Journal Article
• Authors: Ridwan, A; Sari, T N
• Is Part Of: Journal of physics. Volume 1764:Issue 1(2021)
• Publication Date: 2021-02
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1764/1/012047
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Body Mass Index (BMI), one of which is used to indicate the category of nutrition al status of children under five whether it is proportional or not. Anthropometry has an important role to play in determining the nutritional status of Toddler. The Anthropometric guidelines for determining the nutritional status of Toddler are the parameters chosen which include an assessment of height, age, and weight. On the other hand this ever-increasing amount of data requires several methods to process and draw conclusions and information from these data. Some methods used to process large data to find patterns contained therein are simple, including the Naïve Bayes Clasifier Algorithm and C4.5 Algorithm. Both are methods in the classification of Data Mining Techniques. The Naïve Bayes Clasifier Algorithm and C4.5 Algorithm are included in the Ten Most Popular Data Mining Classifications. This Research will apply the Naïve Bayes Clasifier Algorithm and C4.5 Algorithm which is used for the classification of Toddler Nutrition Status based on the Anthropometry Index so as to produce the accuracy of both algorithms. While the five classes will be classified as thin, very thin, normal, fat, very fat. The results of this classification process show that the C4.5 algorithm has an accuracy rate of 0.93% better than the Naïve Bayes Clasifier Algorithm. The algorithm that has been formed can then be developed and implemented into an application making it easier for stakeholders to make a decision for the Classification of Toddler Nutrition Status based on the Anthropometric Index